Social environment and corporate payouts

Abstract

We investigate if macro-level social capital, which captures the notions of trust and honesty, has any effects on corporate payout policy. We find that firms situated in U.S. states (or counties) with higher levels of social capital (SC) have higher dividend payouts (DP). These results survive a battery of robustness tests dealing with inference and various forms of endogeneity. We find that the positive SC-DP association is more prominent when monitoring is weak. We also find that the positive SC-DP association remains when we account for internal social capital, political corruption, federal and state income taxes, and other possible dividend clienteles such as in-state pension funds and pension-age populations. Our study contributes to the literatures that regional locations and social considerations constitute clienteles who affect important corporate strategic decisions such as corporate payouts.

KEYWORDS:

• Dividend payout
• social capital
• corporate social responsibility
• trustworthiness
• external firm monitors

JELs:

• G35
• M14
• G41

Acknowledgments:

We thank the Editor, Associate Editor, two anonymous referees, Anand Jha, Yong Joo Kang (Discussant), David Newton, and Ian Rakita for their constructive comments, and William Schipper for his excellent copyediting. We also thank conference attendees at ASAC 2020 (Finance Division). We acknowledge financial support from Memorial University of Newfoundland (A. Hossain), Senior Concordia University Research Chair in Finance (Kryzanowski), and the Social Sciences and Humanities Research Council of Canada (A. Hossain and Kryzanowski, SSHRC, Grant#430-2020-00275; Kryzanowski, SSHRC, Grant #435-2018-048).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Mitton (2004) finds that firms with stronger corporate governance in countries with stronger investor protection pay higher dividends.

2 This is also reflected in the upgrade of the Critical Finance Review from A to A* in 2019 in the ABDC rankings.

3 These associations include: religious organizations, civic and social associations, business associations, political organizations, professional organizations, labor organizations, bowling centers, fitness and recreational sports centers, and golf course and country clubs.

4 Please go to: https://aese.psu.edu/nercrd/community/social-capital-resources/social-capital-variables-for-2014/data-dictionary-social-capital-variables

5 Detailed calculation: IB for an average firm is $129.71 million. Increase in SC from KS to FL = 1.6040 – (-0.8992) = 2.5032. An increase in SC of 2.5032 causes an increase in DIV/IB of 2.5032*0.0189 = 0.0473, or 0.0473 * 129.71 = $6.14 million. Since the mean DIV/IB is 0.1838 (see Table 1), an increase in DIV/IB of 0.0473 is a 0.0473/0.1838 ∼ 25.7% increase in DIV/IB.

6 We thank an anonymous reviewer for suggesting this test.

7 Although our preferred method is Entropy Balancing, we acknowledge that many in the social sciences still use the PSM approach. For the PSM test, we consider the above-median or high SOCIAL CAPITAL subsample as the treatment group. To calculate the probability (propensity score) of belonging to the high group, we use a logit model where the covariates are the firm- and industry-level controls in our main empirical model (see eq. [1]). We then use nearest-neighbor matching with replacement to select a match for each firm in the treatment group from the below-medium SOCIAL CAPITAL sample to obtain our control sample. Regression results using the PSM sample finds qualitatively similar results. For example, the coefficient for SOCIAL CAPITAL is 0.0242 (p<0.01).

8 One could question why we find firms located in high SC environments. We note that locational decisions are based not only on region-specific determinants but also on firm- and industry-specific location determinants such as informal information spillovers and local labor markets (Arita and McCann 2000), and the many other determinants identified in the review by Jain, Kothari, and Kumar (2016) such as experiential learning, customer relationships, and top-management's or firm's backgrounds and networks.

9 The IV approach to identification has been subject to increasing criticism (see e.g. Jiang 2017).

10 A one standard deviation change in Social Capital (CSR) is associated with an increase in DP of 5.9% (4%) of DP's standard deviation so that the ratio of their effects is 5.9/4.0 ∼ 1.5 times.

11 A one standard deviation change in Social Capital (CORRUPTION) is associated with an increase in DP of 5.9% (2.1%) of DP's standard deviation so that the ratio of their effects is 5.9/2.1 ∼ 2.8 times.

12 In subsample tests, we find that the coefficient (t-stat) for High Active Institutional Ownership is 0.0092 (t = 3.27) and that for Low Active Institutional Ownership is 0.0203 (t = 7.11).

13 In subsample tests, we find that the coefficient (t-stat) for High Analyst is 0.0158 (t = 4.56) and that for Low Analyst is 0.0221 (t = 6.28).

14 In subsample tests, we find that the coefficient (t-stat) for High Leverage is 0.0131 (t = 3.66) and that for Low Leverage is 0.0210 (t = 7.76).

Additional information

Funding

This work was supported by Concordia University: [Grant Number Senior Research Chair in Finance (Kryzanowski)]; Memorial University of Newfoundland: [Grant Number (A. Hossain)]; Social Sciences and Humanities Research Council of Canada: [Grant Number Grant #435-2018-048 (Kryzanowski), Grant#430-2020-00275 (A. Hossain and Kryzanowski)].